Preschoolers naturally form mental representations that capture generic knowledge about object kinds. These have been considered to pose a special explanatory and learning challenge. We here argue for a new deductive model of them, where (i) the representations in question have a linguistic format from the start; (ii) they are inherently structurally simpler compared to reference to individuals or quantifications; and (iii) formed in communicative contexts because communication in humans is linked to language. In this model, specific language‐related resources explain (...) the scope and limits of the forms of knowledge obtained, illustrating how language and cognition develop in tandem. (shrink)

The possibility and extent of thought without language have been subject to much controversy. Insight from non- or minimally verbal humans can inform this debate empirically. Since most such individuals are on the autism spectrum, of which they make up a sizable 25–30%, an important connection between language and autism transpires. Here we propose a model which makes sense of this link and explains why the non-verbal human mind, as present evidence suggests, represents a fundamentally different cognitive phenotype. This model (...) views the relevant part of the autism spectrum as reflecting the breakdown of a cognitive phenotype of which language is an inherent element and which will manifest principled limitations in the latter's absence. (shrink)

While research on object individuation assumes that even very young children are able to perceive objects as particulars, we argue that the results of relevant studies can be explained in terms of feature discrimination. We propose that children start out navigating the world with a feature‐based ontology and only later become able to individuate objects spatiotemporally. Furthermore, object individuation is a cognitively demanding achievement resting on a uniquely human form of enculturation, namely the acquisition of deictic demonstratives. We conclude by (...) outlining empirical expectations for operationalizations of our proposal. (shrink)

Over the past few decades, cognitive science has identified several forms of reasoning that make essential use of conceptual knowledge. Despite significant theoretical and empirical progress, there is still no unified framework for understanding how concepts are used in reasoning. This paper argues that the theory of conceptual spaces is capable of filling this gap. Our strategy is to demonstrate how various inference mechanisms which clearly rely on conceptual information—including similarity, typicality, and diagnosticity-based reasoning—can be modeled using principles derived from (...) conceptual spaces. Our first topic analyzes the role of expectations in inductive reasoning and their relation to the structure of our concepts. We examine the relationship between using generic expressions in natural language and common-sense reasoning as a second topic. We propose that the strength of a generic can be described by distances between properties and prototypes in conceptual spaces. Our third topic is category-based induction. We demonstrate that the theory of conceptual spaces can serve as a comprehensive model for this type of reasoning. The final topic is analogy. We review some proposals in this area, present a taxonomy of analogical relations, and show how to model them in terms of distances in conceptual spaces. We also briefly discuss the implications of the model for reasoning with concepts in artificial systems. (shrink)